Datasets

StartR Workshop

Maik Bieleke, PhD

University of Konstanz

November 24, 2024

Dataset 1: FIFA

https://sofifa.com/ | https://kaggle.com/

Variables

The dataset has the following variables:

Variable Meaning
ID unique ID of the player Name name of the player
Age age of the player Nationality nationality of the player
Club club of the player Reputation reputation of the player (1 to 5)
Height height of the player (in cm) Weight weight of the player (in kg)
Overall overall attribute of the player (43 to 91) Potential potential attribute of the player (42 to 95)
Value value of the player (in €) Wage wage of the player (in €)
Foot preferred foot (left, right)

Dataset

  • fifa.xlsx

Dataset 2: HIIT

https://doi.org/10.1371/journal.pone.0139801

Study

Photo courtesy of @nhoizey

  • 22 male and female team sport athletes (soccer, basketball, handball)
  • 6-day running-based HIIT training with 11 HIIT Sessions
  • physiological and performance tests at pre, post, and follow-up

Variables

The dataset has the following variables:

Variable Explanation Variable Explanation
Sex Sex RSA Repeated Sprint Ability
CMJ Countermovement Jump MRJ Multiple Rebound Jump
RF Rectus Femoris BF Biceps Femoris
Dm Maximal Radial Muscle Belly Displacement Tc Contraction Time between 10 and 90% Dm
CK Creatinkinase CRP C-Reactive Protein
DOMS Delayed Onset Muscle Soreness RPE Rating of Perceived Exertion
  • Sex is a categorical variable (1 = male, 2 = female)
  • RSA to DOMS were measured at _Pre, _Post1, and _Post2
  • RPE was measured once in each HIIT session (_1 to _11)

Dataset

This overall dataset comes in two formats:

  • Wide format: hiit.xlsx
  • Long format: hiit_long.xlsx

Moreover, there are also subsets of the data in wide format:

  • hiit_f_tst.xlsx: Test variables of females
  • hiit_m_tst.xlsx: Test variables of males
  • hiit_f_rpe.xlsx: RPE measures of females
  • hiit_m_rpe.xlsx: RPE measures of males

www.maikbieleke.com